[GitHub] [spark] zhengruifeng commented on pull request #40263: [SPARK-42659][ML] Reimplement `FPGrowthModel.transform` with dataframe operations

2023-03-21 Thread via GitHub 


zhengruifeng commented on PR #40263:
URL: https://github.com/apache/spark/pull/40263#issuecomment-1478764552

   @srowen sounds reasonable


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[GitHub] [spark] zhengruifeng commented on pull request #40263: [SPARK-42659][ML] Reimplement `FPGrowthModel.transform` with dataframe operations

2023-03-20 Thread via GitHub 


zhengruifeng commented on PR #40263:
URL: https://github.com/apache/spark/pull/40263#issuecomment-1477193966

   TL;DR  I want to apply scalar subquery to optimize 
`FPGrowthModel.transform`, there are two options:
   
   1, create temp views and use `spark.sql`, see 
https://github.com/apache/spark/commit/63595ba03d9f18fe0b43bfb09f974ea50cb2c651;
   
   2, add `private[spark] def withScalarSubquery(colName: String, subquery: 
Dataset[_]): DataFrame`, it seems much more convenient but not sure whether it 
is a proper way.
   
   cc @cloud-fan @HyukjinKwon 


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[GitHub] [spark] zhengruifeng commented on pull request #40263: [SPARK-42659][ML] Reimplement `FPGrowthModel.transform` with dataframe operations

2023-03-16 Thread via GitHub 


zhengruifeng commented on PR #40263:
URL: https://github.com/apache/spark/pull/40263#issuecomment-1473064231

   @srowen if the latest performance test seems fine, then I'd ask the SQL guys 
whether we can have a subquery method in DataFrame APIs.


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[GitHub] [spark] zhengruifeng commented on pull request #40263: [SPARK-42659][ML] Reimplement `FPGrowthModel.transform` with dataframe operations

2023-03-14 Thread via GitHub 


zhengruifeng commented on PR #40263:
URL: https://github.com/apache/spark/pull/40263#issuecomment-1467821846

   yes, the `BroadcastNestedLoopJoin` is slower.
   
   Then I have a try with subquery, and it's faster in both execution and 
analysis, but I have to create temp view and write the sql query then, see 
https://github.com/apache/spark/pull/40263/commits/63595ba03d9f18fe0b43bfb09f974ea50cb2c651
   
   `model.transform(df).count()`: 4566 -> 4902
   `model.transform(df).schema`: 2095 -> 298
   
   So I'm trying to add a new method `Dataset.withScalarSubquery` in 
https://github.com/apache/spark/pull/40263/commits/c41ac094eb40520948d95108a78431694a33772d
 
   
   not sure whether it is the correct way to support `ScalarSubquery` in 
DataFrame APIs, but it is actually a pain point to me.
   
   ```
   scala> model.transform(df).explain("extended")
   == Parsed Logical Plan ==
   'Project [items#5, CASE WHEN NOT isnull('items) THEN aggregate('prediction, 
cast(array() as array), lambdafunction(CASE WHEN forall(lambda 
'y_1[antecedent], lambdafunction(array_contains('items, lambda 'x_2), lambda 
'x_2, false)) THEN array_union(lambda 'x_0, array_except(lambda 
'y_1[consequent], 'items)) ELSE lambda 'x_0 END, lambda 'x_0, lambda 'y_1, 
false), lambdafunction(lambda 'x_3, lambda 'x_3, false)) ELSE cast(array() as 
array) END AS prediction#74]
   +- Project [items#5, scalar-subquery#70 [] AS prediction#71]
  :  +- Aggregate [collect_list(struct(antecedent, antecedent#57, 
consequent, consequent#58), 0, 0) AS collect_list(struct(antecedent, 
consequent))#68]
  : +- Filter (NOT isnull(antecedent#57) AND NOT isnull(consequent#58))
  :+- LogicalRDD [antecedent#57, consequent#58, confidence#59, 
lift#60, support#61], false
  +- Project [value#2 AS items#5]
 +- SerializeFromObject [mapobjects(lambdavariable(MapObject, 
ObjectType(class java.lang.Object), true, 1), staticinvoke(class 
org.apache.spark.unsafe.types.UTF8String, StringType, fromString, 
validateexternaltype(lambdavariable(MapObject, ObjectType(class 
java.lang.Object), true, 1), StringType, ObjectType(class java.lang.String)), 
true, false, true), input[0, [Ljava.lang.String;, true], None) AS value#2]
+- ExternalRDD [obj#1]
   
   == Analyzed Logical Plan ==
   items: array, prediction: array
   Project [items#5, CASE WHEN NOT isnull(items#5) THEN 
aggregate(prediction#71, cast(array() as array), lambdafunction(CASE 
WHEN forall(lambda y_1#76.antecedent, lambdafunction(array_contains(items#5, 
lambda x_2#78), lambda x_2#78, false)) THEN array_union(lambda x_0#75, 
array_except(lambda y_1#76.consequent, items#5)) ELSE lambda x_0#75 END, lambda 
x_0#75, lambda y_1#76, false), lambdafunction(lambda x_3#77, lambda x_3#77, 
false)) ELSE cast(array() as array) END AS prediction#74]
   +- Project [items#5, scalar-subquery#70 [] AS prediction#71]
  :  +- Aggregate [collect_list(struct(antecedent, antecedent#57, 
consequent, consequent#58), 0, 0) AS collect_list(struct(antecedent, 
consequent))#68]
  : +- Filter (NOT isnull(antecedent#57) AND NOT isnull(consequent#58))
  :+- LogicalRDD [antecedent#57, consequent#58, confidence#59, 
lift#60, support#61], false
  +- Project [value#2 AS items#5]
 +- SerializeFromObject [mapobjects(lambdavariable(MapObject, 
ObjectType(class java.lang.Object), true, 1), staticinvoke(class 
org.apache.spark.unsafe.types.UTF8String, StringType, fromString, 
validateexternaltype(lambdavariable(MapObject, ObjectType(class 
java.lang.Object), true, 1), StringType, ObjectType(class java.lang.String)), 
true, false, true), input[0, [Ljava.lang.String;, true], None) AS value#2]
+- ExternalRDD [obj#1]
   
   == Optimized Logical Plan ==
   Project [items#5, CASE WHEN isnotnull(items#5) THEN 
aggregate(scalar-subquery#70 [], [], lambdafunction(CASE WHEN forall(lambda 
y_1#76.antecedent, lambdafunction(array_contains(items#5, lambda x_2#78), 
lambda x_2#78, false)) THEN array_union(lambda x_0#75, array_except(lambda 
y_1#76.consequent, items#5)) ELSE lambda x_0#75 END, lambda x_0#75, lambda 
y_1#76, false), lambdafunction(lambda x_3#77, lambda x_3#77, false)) ELSE [] 
END AS prediction#74]
   :  +- Aggregate [collect_list(struct(antecedent, antecedent#57, consequent, 
consequent#58), 0, 0) AS collect_list(struct(antecedent, consequent))#68]
   : +- Project [antecedent#57, consequent#58]
   :+- LogicalRDD [antecedent#57, consequent#58, confidence#59, 
lift#60, support#61], false
   +- InMemoryRelation [items#5], StorageLevel(disk, memory, deserialized, 1 
replicas)
 +- *(1) Project [value#2 AS items#5]
+- *(1) SerializeFromObject [mapobjects(lambdavariable(MapObject, 
ObjectType(class java.lang.Object), true, -1), staticinvoke(class 
org.apache.spark.unsafe.types.UTF8String, StringType, fromString, 
validateexternaltype(lambdavariable(MapObject, ObjectType(class 
java.lang.Object), true, -1), StringType, ObjectType

[GitHub] [spark] zhengruifeng commented on pull request #40263: [SPARK-42659][ML] Reimplement `FPGrowthModel.transform` with dataframe operations

2023-03-13 Thread via GitHub 


zhengruifeng commented on PR #40263:
URL: https://github.com/apache/spark/pull/40263#issuecomment-1465662299

   I did a quick test with dataset `T10I4D100K` in 
http://fimi.uantwerpen.be/data/ 
   
   fit:
   ```
   scala> val df = 
sc.textFile("/Users/ruifeng.zheng/.dev/data/T10I4D100K.dat").map(_.split(" 
")).toDF("items")
   df: org.apache.spark.sql.DataFrame = [items: array]
   
   scala> df.count
   res16: Long = 10
   
   scala> val model = new 
FPGrowth().setMinSupport(0.01).setMinConfidence(0.01).fit(df)
   model: org.apache.spark.ml.fpm.FPGrowthModel = FPGrowthModel: 
uid=fpgrowth_92901252345a, numTrainingRecords=10
   
   scala> model.freqItemsets.count
   res17: Long = 385
   
   
   scala> model.associationRules.count
   res18: Long = 21 
   
   
   scala> model.save("/tmp/fpm.model")
   ```
   
   
   transformation:
   ```
   import org.apache.spark.ml.fpm._
   val df = 
sc.textFile("/Users/ruifeng.zheng/.dev/data/T10I4D100K.dat").map(_.split(" 
")).toDF("items")
   df.cache()
   df.count()
   
   val model = FPGrowthModel.load("/tmp/fpm.model")
   model.transform(df).explain("extended")
   Seq.range(0, 100).foreach{i => model.transform(df).count()} // warms up
   val start = System.currentTimeMillis; Seq.range(0, 100).foreach{i => 
model.transform(df).count()}; val end = System.currentTimeMillis; end - start
   val start = System.currentTimeMillis; Seq.range(0, 100).foreach{i => 
model.transform(df).schema}; val end = System.currentTimeMillis; end - start
   ```
   
   master:
   ```
   master
   ```
   scala> val model = FPGrowthModel.load("/tmp/fpm.model")
   model: org.apache.spark.ml.fpm.FPGrowthModel = FPGrowthModel: 
uid=fpgrowth_92901252345a, numTrainingRecords=10
   
   scala> model.transform(df).explain("extended")
   == Parsed Logical Plan ==
   'Project [items#5, UDF('items) AS prediction#70]
   +- Project [value#2 AS items#5]
  +- SerializeFromObject [mapobjects(lambdavariable(MapObject, 
ObjectType(class java.lang.Object), true, 1), staticinvoke(class 
org.apache.spark.unsafe.types.UTF8String, StringType, fromString, 
validateexternaltype(lambdavariable(MapObject, ObjectType(class 
java.lang.Object), true, 1), StringType, ObjectType(class java.lang.String)), 
true, false, true), input[0, [Ljava.lang.String;, true], None) AS value#2]
 +- ExternalRDD [obj#1]
   
   == Analyzed Logical Plan ==
   items: array, prediction: array
   Project [items#5, UDF(items#5) AS prediction#70]
   +- Project [value#2 AS items#5]
  +- SerializeFromObject [mapobjects(lambdavariable(MapObject, 
ObjectType(class java.lang.Object), true, 1), staticinvoke(class 
org.apache.spark.unsafe.types.UTF8String, StringType, fromString, 
validateexternaltype(lambdavariable(MapObject, ObjectType(class 
java.lang.Object), true, 1), StringType, ObjectType(class java.lang.String)), 
true, false, true), input[0, [Ljava.lang.String;, true], None) AS value#2]
 +- ExternalRDD [obj#1]
   
   == Optimized Logical Plan ==
   Project [items#5, UDF(items#5) AS prediction#70]
   +- InMemoryRelation [items#5], StorageLevel(disk, memory, deserialized, 1 
replicas)
 +- *(1) Project [value#2 AS items#5]
+- *(1) SerializeFromObject [mapobjects(lambdavariable(MapObject, 
ObjectType(class java.lang.Object), true, -1), staticinvoke(class 
org.apache.spark.unsafe.types.UTF8String, StringType, fromString, 
validateexternaltype(lambdavariable(MapObject, ObjectType(class 
java.lang.Object), true, -1), StringType, ObjectType(class java.lang.String)), 
true, false, true), input[0, [Ljava.lang.String;, true], None) AS value#2]
   +- Scan[obj#1]
   
   == Physical Plan ==
   *(1) Project [items#5, UDF(items#5) AS prediction#70]
   +- InMemoryTableScan [items#5]
 +- InMemoryRelation [items#5], StorageLevel(disk, memory, 
deserialized, 1 replicas)
   +- *(1) Project [value#2 AS items#5]
  +- *(1) SerializeFromObject 
[mapobjects(lambdavariable(MapObject, ObjectType(class java.lang.Object), true, 
-1), staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, 
fromString, validateexternaltype(lambdavariable(MapObject, ObjectType(class 
java.lang.Object), true, -1), StringType, ObjectType(class java.lang.String)), 
true, false, true), input[0, [Ljava.lang.String;, true], None) AS value#2]
 +- Scan[obj#1]
   
   
   scala> Seq.range(0, 100).foreach{i => model.transform(df).count()} // warms 
up
   
   scala> val start = System.currentTimeMillis; Seq.range(0, 100).foreach{i => 
model.transform(df).count()}; val end = System.currentTimeMillis; end - start
   start: Long = 1678692855532
   end: Long = 1678692860098
   res4: Long = 4566
   
   scala> val start = System.currentTimeMillis; Seq.range(0, 100).foreach{i => 
model.transform(df).schema}; val end = System.currentTimeMillis; end - start